Cauda EVs miRNAs analysis using data from both runs

Libraries required for data analysis

source("src.R")

Data: read counts table from both runs

counts_total_run3 <- read.table(
  file = "run3_excerpt_counts/exceRpt_miRNA_ReadCounts.txt",
  header = TRUE, sep = "", row.names = 1
)

counts_total_run4 <- read.table(
  file = "run4_excerpt_counts_/exceRpt_miRNA_ReadCounts.txt",
  header = TRUE, sep = "", row.names = 1
)

colnames(counts_total_run4) <- paste0(colnames(counts_total_run4), "_2")

df <- merge(counts_total_run3, counts_total_run4, by = "row.names")
rownames(df) <- df$Row.names
df <- df[, -1]

knitr::kable(head(df))

	MS28F1_sample2	MS28F1_sample3	MS28F1_sample4	MS28F1_sample5	MS28F1_sample6	MS28F1_sample7	MS28F1_sample8	MS28F1_sample9	MS28F1_sample11	MS28F1_sample14	MS28F1_sample2_2	MS28F1_sample3_2	MS28F1_sample4_2	MS28F1_sample5_2	MS28F1_sample6_2	MS28F1_sample7_2	MS28F1_sample8_2	MS28F1_sample9_2	MS28F1_sample11_2	MS28F1_sample14_2
mmu-let-7a-1	1.1666667	1.5	1.666667	1.1666667	1.3333333	0.000	0.000	2.000000	7.5833333	4.1666667	2.9166667	1.750	1.000	1.500	0.9166667	0.8333333	1.916667	2	3.333333	4.0000000
mmu-let-7a-1-3p\|mmu-let-7c-2-3p	19.0000000	34.0	21.500000	38.0000000	42.0000000	14.000	26.000	42.000000	95.0000000	78.0000000	32.0000000	53.000	31.000	42.000	20.0000000	32.0000000	45.000000	65	103.500000	90.5000000
mmu-let-7a-2	0.6666667	1.0	2.666667	0.6666667	0.3333333	0.000	0.000	3.166667	0.5833333	0.6666667	2.9166667	1.750	0.000	0.500	1.9166667	1.3333333	2.416667	5	2.500000	0.3333333
mmu-let-7a-5p	819.5000000	2422.5	1568.833333	2313.3333333	1867.6666667	1422.833	1469.583	4181.500000	5725.0000000	4915.0000000	3184.1666667	4260.667	2186.333	4359.667	2123.3333333	3710.6666667	4274.666667	5608	7622.750000	7111.8333333
mmu-let-7b	0.0000000	0.0	0.000000	0.0000000	0.0000000	0.000	0.250	0.000000	0.0000000	0.0000000	0.3333333	0.000	0.000	0.000	0.0000000	0.0000000	0.000000	0	0.250000	0.0000000
mmu-let-7b-3p	10.0000000	7.0	9.000000	6.0000000	10.0000000	2.000	4.000	10.000000	18.0000000	23.0000000	10.0000000	7.000	10.000	17.000	0.0000000	7.0000000	9.000000	10	22.000000	22.0000000

Specifying the group & batch while filterByExprs()

counts_total <- df
d <- data.frame(
  row.names = colnames(counts_total),
  group = c(
    "MSUS", "CTRL", "MSUS", "CTRL", "MSUS",
    "CTRL", "MSUS", "CTRL", "CTRL", "MSUS",
    "MSUS", "CTRL", "MSUS", "CTRL", "MSUS",
    "CTRL", "MSUS", "CTRL", "CTRL", "MSUS"
  ),
  batch = c(
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2
  )
)

counts_total[is.na(counts_total)] <- 0

dds <- calcNormFactors(DGEList(counts_total), method = "TMM")

dds$samples$group <- c(
  "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "CTRL",
  "MSUS", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL", "MSUS", "CTRL",
  "CTRL", "MSUS"
)

dds$samples$batch <- c(
  1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
  2, 2, 2, 2, 2, 2, 2, 2, 2, 2
)

mm <- model.matrix(~ batch + group, data = d)

### specifying thebatch while filtering by expression
dds <- dds[which(filterByExpr(dds, design = mm, min.count = 20, min.prop = 0.8)), ]

# normalization by counts per million
en <- log1p(edgeR::cpm(dds))

PCA plots

Samples colored by group and shaped by batched

plgINS::plPCA(en,
  colorBy = dds$samples$group, add.labels = FALSE,
  points.size = 12, shapeBy = paste0("Batch", dds$samples$batch)
)

Samples colored by library sizes and shaped by group

plgINS::plPCA(en,
  colorBy = dds$samples$lib.size, add.labels = FALSE,
  points.size = 12, shapeBy = dds$samples$group
)

Samples colored by library sizes and shaped by batches

plgINS::plPCA(en,
  colorBy = dds$samples$lib.size, add.labels = FALSE,
  points.size = 12, shapeBy = dds$samples$batch
)

Without RUVSeq:

mm <- model.matrix(~ dds$samples$batch + dds$samples$group, data = d)
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "dds$samples$groupMSUS")
res_without_ruvseq <- as.data.frame(topTags(fit, Inf))
knitr::kable(head(res_without_ruvseq, n = 20))

	logFC	logCPM	LR	PValue	FDR
mmu-miR-31-5p	-0.5630570	9.993834	14.030932	0.0001798	0.0341794
mmu-miR-878-5p	0.5510667	7.622078	12.917534	0.0003255	0.0341794
mmu-miR-155-5p	-0.5886507	7.892908	12.102538	0.0005035	0.0352473
mmu-miR-34c-5p	0.4318036	12.490877	9.725028	0.0018178	0.0954321
mmu-let-7a-5p	-0.3683619	13.378312	9.099385	0.0025570	0.1073921
mmu-let-7c-5p	-0.3242745	14.170447	8.286976	0.0039930	0.1338609
mmu-miR-881-3p	0.5117907	11.466915	8.085521	0.0044620	0.1338609
mmu-miR-484	-0.5904084	7.027135	7.268392	0.0070179	0.1663234
mmu-let-7d-5p	-0.3214286	12.059820	6.929838	0.0084769	0.1663234
mmu-miR-223-3p	-0.4271443	7.623028	6.902397	0.0086080	0.1663234
mmu-let-7b-5p	-0.3097913	12.513706	6.880898	0.0087122	0.1663234
mmu-let-7f-5p	-0.2362616	15.094629	6.637108	0.0099876	0.1747829
mmu-miR-339-5p	-0.3058132	8.661776	6.150944	0.0131342	0.2121678
mmu-let-7g-5p	-0.1996120	14.499044	5.439798	0.0196829	0.2480974
mmu-miR-9-5p	0.3439249	7.798056	5.402020	0.0201135	0.2480974
mmu-miR-381-3p	0.6938547	6.549209	5.298593	0.0213427	0.2480974
mmu-miR-150-5p	-0.4484746	10.173697	5.222411	0.0222976	0.2480974
mmu-miR-690	-0.3084864	8.671606	5.112553	0.0237533	0.2480974
mmu-miR-218-5p	0.2785387	8.825840	5.069562	0.0243495	0.2480974
mmu-miR-148a-5p	-0.3500173	6.582395	5.044113	0.0247098	0.2480974

Differential expression analysis with RUVSeq (SV=1)

re <- RUVSeq::RUVs(en,
  cIdx = row.names(en), k = 1, scIdx =
    RUVSeq::makeGroups(d$group), isLog = TRUE
)
d$SV1 <- re$W[, 1]

mm <- model.matrix(~ d$SV1 + dds$samples$batch + dds$samples$group, data = d)

dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "dds$samples$groupMSUS")
res_with_ruvseq1 <- as.data.frame(topTags(fit, Inf))
knitr::kable(head(res_with_ruvseq1, n = 20))

	logFC	logCPM	LR	PValue	FDR
mmu-miR-871-3p	0.4224445	7.865090	11.917353	0.0005561	0.0721259
mmu-miR-31-5p	-0.3876365	9.998799	11.067851	0.0008784	0.0721259
mmu-miR-155-5p	-0.4509784	7.903016	10.270414	0.0013518	0.0721259
mmu-miR-881-3p	0.2898815	11.465624	9.947026	0.0016111	0.0721259
mmu-miR-878-5p	0.4517734	7.616360	9.829572	0.0017173	0.0721259
mmu-let-7f-5p	-0.2539420	15.094684	8.265118	0.0040414	0.1414491
mmu-let-7a-5p	-0.3252566	13.378699	7.854573	0.0050692	0.1507945
mmu-miR-34c-5p	0.3699764	12.490816	7.628736	0.0057446	0.1507945
mmu-let-7c-5p	-0.2660684	14.170724	6.590841	0.0102505	0.2391777
mmu-let-7d-5p	-0.2957245	12.060525	6.254407	0.0123885	0.2514734
mmu-miR-148a-5p	-0.3860498	6.582776	6.099744	0.0135201	0.2514734
mmu-miR-486a-5p\|mmu-miR-486b-5p	0.4242328	9.175211	5.992121	0.0143699	0.2514734
mmu-miR-223-3p	-0.3918551	7.627599	5.758552	0.0164086	0.2650618
mmu-miR-142a-5p	-0.3388176	7.860399	5.331804	0.0209397	0.2995186
mmu-miR-484	-0.4464519	7.035887	5.294393	0.0213942	0.2995186
mmu-miR-182-5p	-0.1858805	11.537582	5.084665	0.0241383	0.3168156
mmu-miR-183-5p	-0.2106265	11.356649	4.759219	0.0291418	0.3395679
mmu-let-7g-5p	-0.1769275	14.499171	4.704360	0.0300862	0.3395679
mmu-miR-181d-5p	0.2729988	7.492399	4.642132	0.0311962	0.3395679
mmu-miR-467a-5p\|mmu-miR-467b-5p	-0.3391907	6.822511	4.509472	0.0337076	0.3395679

PCA plot with 1 SV

Samples colored by group and shaped by batched

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$group, add.labels = FALSE,
  points.size = 12, shapeBy = paste0("Batch", dds$samples$batch)
)

Samples colored by library sizes and shaped by group

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$lib.size, add.labels = FALSE,
  points.size = 12, shapeBy = dds$samples$group
)

Samples colored by library sizes and shaped by batches

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$lib.size, add.labels = FALSE,
  points.size = 12, shapeBy = dds$samples$batch
)

References

report::cite_packages(sessionInfo())

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SessionInfo

devtools::session_info() %>%
  details::details()


─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.4 (2021-02-15)
 os       Ubuntu 16.04.7 LTS          
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  de_DE.UTF-8                 
 ctype    de_DE.UTF-8                 
 tz       Europe/Zurich               
 date     2021-06-28                  

─ Packages ───────────────────────────────────────────────────────────────────
 ! package                * version  date       lib source        
   annotate                 1.68.0   2020-10-27 [1] Bioconductor  
   AnnotationDbi          * 1.52.0   2020-10-27 [1] Bioconductor  
   AnnotationFilter         1.14.0   2020-10-27 [1] Bioconductor  
   AnnotationHub            2.22.1   2021-04-16 [1] Bioconductor  
   aroma.light              3.20.0   2020-10-27 [1] Bioconductor  
   ash                      1.0-15   2015-09-01 [1] CRAN (R 4.0.4)
   askpass                  1.1      2019-01-13 [1] CRAN (R 4.0.4)
   assertthat               0.2.1    2019-03-21 [1] CRAN (R 4.0.4)
   backports                1.2.1    2020-12-09 [1] CRAN (R 4.0.4)
   bayestestR               0.9.0    2021-04-08 [1] CRAN (R 4.0.4)
   beeswarm                 0.3.1    2021-03-07 [1] CRAN (R 4.0.4)
   Biobase                * 2.50.0   2020-10-27 [1] Bioconductor  
   BiocFileCache            1.14.0   2020-10-27 [1] Bioconductor  
   BiocGenerics           * 0.36.1   2021-04-16 [1] Bioconductor  
   BiocManager              1.30.12  2021-03-28 [1] CRAN (R 4.0.4)
   BiocParallel           * 1.24.1   2020-11-06 [1] Bioconductor  
   BiocVersion              3.12.0   2020-04-27 [1] Bioconductor  
   biomaRt                * 2.46.3   2021-02-09 [1] Bioconductor  
   Biostrings             * 2.58.0   2020-10-27 [1] Bioconductor  
   bit                      4.0.4    2020-08-04 [1] CRAN (R 4.0.4)
   bit64                    4.0.5    2020-08-30 [1] CRAN (R 4.0.4)
   bitops                   1.0-7    2021-04-24 [1] CRAN (R 4.0.4)
   blob                     1.2.1    2020-01-20 [1] CRAN (R 4.0.4)
   bookdown                 0.22     2021-04-22 [1] CRAN (R 4.0.4)
   boot                     1.3-27   2021-02-12 [1] CRAN (R 4.0.4)
   broom                    0.7.7    2021-06-13 [1] CRAN (R 4.0.4)
   bslib                    0.2.4    2021-01-25 [1] CRAN (R 4.0.4)
   cachem                   1.0.4    2021-02-13 [1] CRAN (R 4.0.4)
   callr                    3.7.0    2021-04-20 [1] CRAN (R 4.0.4)
   caTools                  1.18.2   2021-03-28 [1] CRAN (R 4.0.4)
   cellranger               1.1.0    2016-07-27 [1] CRAN (R 4.0.4)
   cli                      2.5.0    2021-04-26 [1] CRAN (R 4.0.4)
   clipr                    0.7.1    2020-10-08 [1] CRAN (R 4.0.4)
   coda                     0.19-4   2020-09-30 [1] CRAN (R 4.0.4)
   codetools                0.2-18   2020-11-04 [1] CRAN (R 4.0.4)
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[1] /home/ubuntu/R/x86_64-pc-linux-gnu-library/4.0
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library

 P ── Loaded and on-disk path mismatch.